Публична лекция на тема „Programming with Big Code: Probabilistic Learning

Този четвъртък (17.12.2015 г.) от 17:00 в зала 325, д-р Мартин Вечев от ETH-Цюрих ще изнесе публична лекция на тема „Programming with Big Code: Probabilistic Learning, Program Analysis and Synthesis“.

Абстракт:
The increased availability of massive codebases (e.g., GitHub), a term referred to as „Big Code'', creates an exciting opportunity for new kinds of programming tools based on probabilistic models. Enabled by these models, tomorrow’s tools will provide statistically likely solutions to programming tasks that are difficult or impossible to solve with traditional techniques.
In this talk, I will present a new approach for building such tools based on structured prediction with graphical models, and in particular, Conditional Random Fields (CRFs). These are powerful machine learning techniques popular in computer vision – by connecting these techniques to programs, our work enables new applications not previously possible. As an example, I will discuss JSNice (http://jsnice.org), a system that automatically de-minifies JavaScript programs by predicting statistically likely variable names and types. Since its release, JSNice has become a popular tool in the JavaScript community: it has been used by more than 100,000 users and in every country worldwide.

Кратка биография на лектора:
Martin Vechev is a tenure-track professor at the Department of Computer Science at ETH Zurich where he leads the Software Reliability Lab (http://www.srl.inf.ethz.ch/). Prior to joining ETH in 2012, he was a Research Staff Member at the IBM T.J. Watson Research Center in New York, USA (2007-2011). Before that he obtained his PhD at the University of Cambridge, England. His research interests are in programming languages, program analysis, program synthesis and machine learning. He is the recipient of various awards, including Google Faculty Award (twice), Facebook Faculty award,  Best paper and Outstanding artifact awards, John Atanasoff award, as well as Outstanding and Extraordinary Research Accomplishment awards at IBM Research.